Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations8582
Missing cells3364
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory947.2 KiB
Average record size in memory113.0 B

Variable types

Text6
Numeric6
Boolean1
DateTime1
Categorical1

Alerts

album_total_tracks is highly overall correlated with track_numberHigh correlation
artist_followers is highly overall correlated with artist_popularityHigh correlation
artist_popularity is highly overall correlated with artist_followersHigh correlation
track_number is highly overall correlated with album_total_tracksHigh correlation
artist_genres has 3361 (39.2%) missing valuesMissing
track_id has unique valuesUnique
track_popularity has 503 (5.9%) zerosZeros

Reproduction

Analysis started2025-11-26 19:34:16.413038
Analysis finished2025-11-26 19:34:21.685790
Duration5.27 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

track_id
Text

Unique 

Distinct8582
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:21.887908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters188804
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8582 ?
Unique (%)100.0%

Sample

1st row3EJS5LyekDim1Tf5rBFmZl
2nd row1oQW6G2ZiwMuHqlPpP27DB
3rd row7mdkjzoIYlf1rx9EtBpGmU
4th row67rW0Zl7oB3qEpD5YWWE5w
5th row15xptTfRBrjsppW0INUZjf
ValueCountFrequency (%)
3ejs5lyekdim1tf5rbfmzl1
 
< 0.1%
0xqqpq855n5rnusfbxo2ml1
 
< 0.1%
15xpttfrbrjsppw0inuzjf1
 
< 0.1%
4ccpcczyseq8vrpmk1eds01
 
< 0.1%
3qoq3hqxtajgel9lbnmbyp1
 
< 0.1%
1yezbdt417sfolpqzaohs21
 
< 0.1%
4pz949nfw5surwze0tse7i1
 
< 0.1%
0l0lgwfz7utbnrnqvsbty61
 
< 0.1%
5mfazwer4javdnsy0hhuyh1
 
< 0.1%
4v1sh9y2z6e8hpznoet5rc1
 
< 0.1%
Other values (8572)8572
99.9%
2025-11-26T23:34:22.275740image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64075
 
2.2%
44057
 
2.1%
24047
 
2.1%
53986
 
2.1%
03945
 
2.1%
13945
 
2.1%
33928
 
2.1%
73924
 
2.1%
r3063
 
1.6%
K3025
 
1.6%
Other values (52)150809
79.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
64075
 
2.2%
44057
 
2.1%
24047
 
2.1%
53986
 
2.1%
03945
 
2.1%
13945
 
2.1%
33928
 
2.1%
73924
 
2.1%
r3063
 
1.6%
K3025
 
1.6%
Other values (52)150809
79.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
64075
 
2.2%
44057
 
2.1%
24047
 
2.1%
53986
 
2.1%
03945
 
2.1%
13945
 
2.1%
33928
 
2.1%
73924
 
2.1%
r3063
 
1.6%
K3025
 
1.6%
Other values (52)150809
79.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
64075
 
2.2%
44057
 
2.1%
24047
 
2.1%
53986
 
2.1%
03945
 
2.1%
13945
 
2.1%
33928
 
2.1%
73924
 
2.1%
r3063
 
1.6%
K3025
 
1.6%
Other values (52)150809
79.9%
Distinct7462
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:22.632767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length99
Median length81
Mean length17.032393
Min length1

Characters and Unicode

Total characters146172
Distinct characters254
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6629 ?
Unique (%)77.2%

Sample

1st rowTrippy Mane (ft. Project Pat)
2nd rowOMG!
3rd rowHard 2 Find
4th rowStill Get Like That (ft. Project Pat & Starrah)
5th rowride me like a harley
ValueCountFrequency (%)
the1036
 
3.7%
938
 
3.4%
feat638
 
2.3%
you508
 
1.8%
me396
 
1.4%
i380
 
1.4%
of333
 
1.2%
a331
 
1.2%
love311
 
1.1%
my234
 
0.8%
Other values (6103)22526
81.5%
2025-11-26T23:34:23.172743image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19048
 
13.0%
e13477
 
9.2%
a8637
 
5.9%
o8467
 
5.8%
i7083
 
4.8%
n6683
 
4.6%
t6679
 
4.6%
r6508
 
4.5%
s4720
 
3.2%
l4660
 
3.2%
Other values (244)60210
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)146172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19048
 
13.0%
e13477
 
9.2%
a8637
 
5.9%
o8467
 
5.8%
i7083
 
4.8%
n6683
 
4.6%
t6679
 
4.6%
r6508
 
4.5%
s4720
 
3.2%
l4660
 
3.2%
Other values (244)60210
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)146172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19048
 
13.0%
e13477
 
9.2%
a8637
 
5.9%
o8467
 
5.8%
i7083
 
4.8%
n6683
 
4.6%
t6679
 
4.6%
r6508
 
4.5%
s4720
 
3.2%
l4660
 
3.2%
Other values (244)60210
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)146172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19048
 
13.0%
e13477
 
9.2%
a8637
 
5.9%
o8467
 
5.8%
i7083
 
4.8%
n6683
 
4.6%
t6679
 
4.6%
r6508
 
4.5%
s4720
 
3.2%
l4660
 
3.2%
Other values (244)60210
41.2%

track_number
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7725472
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:23.337035image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q39
95-th percentile17
Maximum102
Range101
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.052792
Coefficient of variation (CV)1.0485479
Kurtosis16.507948
Mean5.7725472
Median Absolute Deviation (MAD)3
Skewness2.6531938
Sum49540
Variance36.636291
MonotonicityNot monotonic
2025-11-26T23:34:23.503818image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12708
31.6%
2814
 
9.5%
3686
 
8.0%
4564
 
6.6%
5480
 
5.6%
6423
 
4.9%
7363
 
4.2%
9338
 
3.9%
8336
 
3.9%
10309
 
3.6%
Other values (44)1561
18.2%
ValueCountFrequency (%)
12708
31.6%
2814
 
9.5%
3686
 
8.0%
4564
 
6.6%
5480
 
5.6%
6423
 
4.9%
7363
 
4.2%
8336
 
3.9%
9338
 
3.9%
10309
 
3.6%
ValueCountFrequency (%)
1021
 
< 0.1%
701
 
< 0.1%
661
 
< 0.1%
571
 
< 0.1%
541
 
< 0.1%
521
 
< 0.1%
491
 
< 0.1%
472
< 0.1%
462
< 0.1%
453
< 0.1%

track_popularity
Real number (ℝ)

Zeros 

Distinct98
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.356211
Minimum0
Maximum99
Zeros503
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:23.665409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139
median58
Q371
95-th percentile82
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation23.816076
Coefficient of variation (CV)0.4548854
Kurtosis-0.23754128
Mean52.356211
Median Absolute Deviation (MAD)15
Skewness-0.78484393
Sum449321
Variance567.20546
MonotonicityNot monotonic
2025-11-26T23:34:23.830665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0503
 
5.9%
62195
 
2.3%
71190
 
2.2%
67188
 
2.2%
70182
 
2.1%
69177
 
2.1%
65174
 
2.0%
68174
 
2.0%
72173
 
2.0%
75173
 
2.0%
Other values (88)6453
75.2%
ValueCountFrequency (%)
0503
5.9%
1102
 
1.2%
269
 
0.8%
339
 
0.5%
430
 
0.3%
524
 
0.3%
623
 
0.3%
78
 
0.1%
817
 
0.2%
916
 
0.2%
ValueCountFrequency (%)
991
 
< 0.1%
971
 
< 0.1%
952
 
< 0.1%
943
 
< 0.1%
937
 
0.1%
9210
0.1%
9113
0.2%
9010
0.1%
8913
0.2%
8819
0.2%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
False
6434 
True
2148 
ValueCountFrequency (%)
False6434
75.0%
True2148
 
25.0%
2025-11-26T23:34:23.976299image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Distinct2547
Distinct (%)29.7%
Missing3
Missing (%)< 0.1%
Memory size67.2 KiB
2025-11-26T23:34:24.376140image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length41
Median length32
Mean length10.522322
Min length1

Characters and Unicode

Total characters90271
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1581 ?
Unique (%)18.4%

Sample

1st rowDiplo
2nd rowYelawolf
3rd rowRiff Raff
4th rowDiplo
5th rowRumelis
ValueCountFrequency (%)
the530
 
3.4%
taylor327
 
2.1%
swift324
 
2.1%
weeknd141
 
0.9%
lil116
 
0.7%
lana99
 
0.6%
del99
 
0.6%
rey99
 
0.6%
ariana94
 
0.6%
grande94
 
0.6%
Other values (3438)13591
87.6%
2025-11-26T23:34:24.969705image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a8012
 
8.9%
e7910
 
8.8%
6935
 
7.7%
i6024
 
6.7%
n5187
 
5.7%
r5166
 
5.7%
o4570
 
5.1%
l4166
 
4.6%
s3108
 
3.4%
t2872
 
3.2%
Other values (124)36321
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)90271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8012
 
8.9%
e7910
 
8.8%
6935
 
7.7%
i6024
 
6.7%
n5187
 
5.7%
r5166
 
5.7%
o4570
 
5.1%
l4166
 
4.6%
s3108
 
3.4%
t2872
 
3.2%
Other values (124)36321
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8012
 
8.9%
e7910
 
8.8%
6935
 
7.7%
i6024
 
6.7%
n5187
 
5.7%
r5166
 
5.7%
o4570
 
5.1%
l4166
 
4.6%
s3108
 
3.4%
t2872
 
3.2%
Other values (124)36321
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8012
 
8.9%
e7910
 
8.8%
6935
 
7.7%
i6024
 
6.7%
n5187
 
5.7%
r5166
 
5.7%
o4570
 
5.1%
l4166
 
4.6%
s3108
 
3.4%
t2872
 
3.2%
Other values (124)36321
40.2%

artist_popularity
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.730016
Minimum0
Maximum100
Zeros25
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:25.131124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q160
median74
Q384
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.645979
Coefficient of variation (CV)0.28174351
Kurtosis1.026035
Mean69.730016
Median Absolute Deviation (MAD)11
Skewness-1.0392656
Sum598423
Variance385.96451
MonotonicityNot monotonic
2025-11-26T23:34:25.301111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88428
 
5.0%
81365
 
4.3%
85328
 
3.8%
100324
 
3.8%
83279
 
3.3%
82239
 
2.8%
84229
 
2.7%
90223
 
2.6%
76215
 
2.5%
78209
 
2.4%
Other values (86)5743
66.9%
ValueCountFrequency (%)
025
0.3%
14
 
< 0.1%
213
0.2%
37
 
0.1%
414
0.2%
58
 
0.1%
68
 
0.1%
78
 
0.1%
818
0.2%
98
 
0.1%
ValueCountFrequency (%)
100324
3.8%
95140
 
1.6%
9446
 
0.5%
9395
 
1.1%
9189
 
1.0%
90223
2.6%
8999
 
1.2%
88428
5.0%
8771
 
0.8%
86142
 
1.7%

artist_followers
Real number (ℝ)

High correlation 

Distinct3740
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24034719
Minimum0
Maximum1.4554214 × 108
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:25.462708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3567.7
Q1462320
median6105547
Q327252551
95-th percentile1.1869218 × 108
Maximum1.4554214 × 108
Range1.4554214 × 108
Interquartile range (IQR)26790231

Descriptive statistics

Standard deviation38031805
Coefficient of variation (CV)1.5823694
Kurtosis2.854826
Mean24034719
Median Absolute Deviation (MAD)6070837
Skewness1.9611416
Sum2.0626596 × 1011
Variance1.4464182 × 1015
MonotonicityNot monotonic
2025-11-26T23:34:25.637015image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145396321137
 
1.6%
14548937190
 
1.0%
14544356785
 
1.0%
11291813760
 
0.7%
5177365044
 
0.5%
10303938841
 
0.5%
2159156939
 
0.5%
4771698838
 
0.4%
11299543938
 
0.4%
2338408836
 
0.4%
Other values (3730)7974
92.9%
ValueCountFrequency (%)
08
0.1%
21
 
< 0.1%
33
 
< 0.1%
44
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
108
0.1%
122
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
14554213612
 
0.1%
14548937190
1.0%
14544356785
1.0%
145396321137
1.6%
12280277714
 
0.2%
12278946012
 
0.1%
12277329227
 
0.3%
1187974511
 
< 0.1%
1187603117
 
0.1%
11872863317
 
0.2%

artist_genres
Text

Missing 

Distinct661
Distinct (%)12.7%
Missing3361
Missing (%)39.2%
Memory size67.2 KiB
2025-11-26T23:34:25.980327image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length120
Median length79
Mean length19.568473
Min length3

Characters and Unicode

Total characters102167
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)6.5%

Sample

1st rowmoombahton
2nd rowcountry hip hop, southern hip hop
3rd rowmoombahton
4th rowdark r&b
5th rowdark r&b
ValueCountFrequency (%)
pop2496
 
15.2%
rock1099
 
6.7%
country1008
 
6.1%
hop764
 
4.7%
hip757
 
4.6%
rap666
 
4.1%
r&b626
 
3.8%
alternative584
 
3.6%
indie543
 
3.3%
metal474
 
2.9%
Other values (338)7411
45.1%
2025-11-26T23:34:26.498511image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11207
 
11.0%
o9604
 
9.4%
p8988
 
8.8%
r7316
 
7.2%
a6752
 
6.6%
t6126
 
6.0%
e5775
 
5.7%
,5606
 
5.5%
n5192
 
5.1%
i4902
 
4.8%
Other values (26)30699
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)102167
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11207
 
11.0%
o9604
 
9.4%
p8988
 
8.8%
r7316
 
7.2%
a6752
 
6.6%
t6126
 
6.0%
e5775
 
5.7%
,5606
 
5.5%
n5192
 
5.1%
i4902
 
4.8%
Other values (26)30699
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)102167
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11207
 
11.0%
o9604
 
9.4%
p8988
 
8.8%
r7316
 
7.2%
a6752
 
6.6%
t6126
 
6.0%
e5775
 
5.7%
,5606
 
5.5%
n5192
 
5.1%
i4902
 
4.8%
Other values (26)30699
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)102167
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11207
 
11.0%
o9604
 
9.4%
p8988
 
8.8%
r7316
 
7.2%
a6752
 
6.6%
t6126
 
6.0%
e5775
 
5.7%
,5606
 
5.5%
n5192
 
5.1%
i4902
 
4.8%
Other values (26)30699
30.0%
Distinct5205
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:26.813180image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters188804
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4295 ?
Unique (%)50.0%

Sample

1st row5QRFnGnBeMGePBKF2xTz5z
2nd row4SUmmwnv0xTjRcLdjczGg2
3rd row3E3zEAL8gUYWaLYB9L7gbp
4th row5QRFnGnBeMGePBKF2xTz5z
5th row06FDIpSHYmZAZoyuYtc7kd
ValueCountFrequency (%)
3ffgbuutkwn1c4f0cjr4uh70
 
0.8%
1mpaxutvl2ej5x0jhispq846
 
0.5%
5h7ixxzfsnmgbie5obspcb31
 
0.4%
2xgeygu76kj55odhwynx0s27
 
0.3%
4hdok0oajd57sgit8xuwjh26
 
0.3%
1atl5glyefjaxhqzspvrlx25
 
0.3%
43wfm1hquliy3iwkwzpn4y25
 
0.3%
2qredhp5rmkjij1i8vgdgr24
 
0.3%
3rqqmkqevncy4prgke6oc523
 
0.3%
5voerutrghtbkelufwymwu23
 
0.3%
Other values (5195)8262
96.3%
2025-11-26T23:34:27.246309image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
54279
 
2.3%
64117
 
2.2%
44038
 
2.1%
34016
 
2.1%
03963
 
2.1%
13864
 
2.0%
23841
 
2.0%
73617
 
1.9%
J3214
 
1.7%
F3178
 
1.7%
Other values (52)150677
79.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54279
 
2.3%
64117
 
2.2%
44038
 
2.1%
34016
 
2.1%
03963
 
2.1%
13864
 
2.0%
23841
 
2.0%
73617
 
1.9%
J3214
 
1.7%
F3178
 
1.7%
Other values (52)150677
79.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54279
 
2.3%
64117
 
2.2%
44038
 
2.1%
34016
 
2.1%
03963
 
2.1%
13864
 
2.0%
23841
 
2.0%
73617
 
1.9%
J3214
 
1.7%
F3178
 
1.7%
Other values (52)150677
79.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)188804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54279
 
2.3%
64117
 
2.2%
44038
 
2.1%
34016
 
2.1%
03963
 
2.1%
13864
 
2.0%
23841
 
2.0%
73617
 
1.9%
J3214
 
1.7%
F3178
 
1.7%
Other values (52)150677
79.8%
Distinct4870
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:27.717051image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length99
Median length83
Mean length20.944419
Min length1

Characters and Unicode

Total characters179745
Distinct characters308
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3824 ?
Unique (%)44.6%

Sample

1st rowd00mscrvll, Vol. 1
2nd rowOMG!
3rd rowHard 2 Find
4th rowd00mscrvll, Vol. 1
5th rowcome closer / ride me like a harley
ValueCountFrequency (%)
the1732
 
5.8%
deluxe729
 
2.4%
original564
 
1.9%
soundtrack560
 
1.9%
of541
 
1.8%
edition491
 
1.6%
442
 
1.5%
motion360
 
1.2%
picture356
 
1.2%
a302
 
1.0%
Other values (4788)23983
79.8%
2025-11-26T23:34:28.389269image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21464
 
11.9%
e15663
 
8.7%
o10147
 
5.6%
i10121
 
5.6%
a9115
 
5.1%
n8819
 
4.9%
r8814
 
4.9%
t7636
 
4.2%
l6195
 
3.4%
s5781
 
3.2%
Other values (298)75990
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)179745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21464
 
11.9%
e15663
 
8.7%
o10147
 
5.6%
i10121
 
5.6%
a9115
 
5.1%
n8819
 
4.9%
r8814
 
4.9%
t7636
 
4.2%
l6195
 
3.4%
s5781
 
3.2%
Other values (298)75990
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)179745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21464
 
11.9%
e15663
 
8.7%
o10147
 
5.6%
i10121
 
5.6%
a9115
 
5.1%
n8819
 
4.9%
r8814
 
4.9%
t7636
 
4.2%
l6195
 
3.4%
s5781
 
3.2%
Other values (298)75990
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)179745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21464
 
11.9%
e15663
 
8.7%
o10147
 
5.6%
i10121
 
5.6%
a9115
 
5.1%
n8819
 
4.9%
r8814
 
4.9%
t7636
 
4.2%
l6195
 
3.4%
s5781
 
3.2%
Other values (298)75990
42.3%
Distinct2384
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
Minimum1952-09-12 00:00:00
Maximum2025-10-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-26T23:34:28.550297image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:28.840647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

album_total_tracks
Real number (ℝ)

High correlation 

Distinct82
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.789443
Minimum1
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:28.995489image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median13
Q317
95-th percentile31
Maximum181
Range180
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.887131
Coefficient of variation (CV)0.86204573
Kurtosis18.467315
Mean13.789443
Median Absolute Deviation (MAD)5
Skewness2.8721505
Sum118341
Variance141.30387
MonotonicityNot monotonic
2025-11-26T23:34:29.164903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11627
19.0%
12657
 
7.7%
16534
 
6.2%
13528
 
6.2%
14500
 
5.8%
11452
 
5.3%
17446
 
5.2%
15444
 
5.2%
10419
 
4.9%
18300
 
3.5%
Other values (72)2675
31.2%
ValueCountFrequency (%)
11627
19.0%
2193
 
2.2%
3132
 
1.5%
489
 
1.0%
581
 
0.9%
673
 
0.9%
779
 
0.9%
884
 
1.0%
9133
 
1.5%
10419
 
4.9%
ValueCountFrequency (%)
1811
 
< 0.1%
1442
< 0.1%
1371
 
< 0.1%
1361
 
< 0.1%
1311
 
< 0.1%
1004
< 0.1%
991
 
< 0.1%
922
< 0.1%
852
< 0.1%
841
 
< 0.1%

album_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.2 KiB
album
5856 
single
2219 
compilation
 
507

Length

Max length11
Median length5
Mean length5.6130273
Min length5

Characters and Unicode

Total characters48171
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbum
2nd rowsingle
3rd rowsingle
4th rowalbum
5th rowsingle

Common Values

ValueCountFrequency (%)
album5856
68.2%
single2219
 
25.9%
compilation507
 
5.9%

Length

2025-11-26T23:34:29.323862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T23:34:29.469034image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
album5856
68.2%
single2219
 
25.9%
compilation507
 
5.9%

Most occurring characters

ValueCountFrequency (%)
l8582
17.8%
a6363
13.2%
m6363
13.2%
b5856
12.2%
u5856
12.2%
i3233
 
6.7%
n2726
 
5.7%
s2219
 
4.6%
g2219
 
4.6%
e2219
 
4.6%
Other values (4)2535
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)48171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l8582
17.8%
a6363
13.2%
m6363
13.2%
b5856
12.2%
u5856
12.2%
i3233
 
6.7%
n2726
 
5.7%
s2219
 
4.6%
g2219
 
4.6%
e2219
 
4.6%
Other values (4)2535
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l8582
17.8%
a6363
13.2%
m6363
13.2%
b5856
12.2%
u5856
12.2%
i3233
 
6.7%
n2726
 
5.7%
s2219
 
4.6%
g2219
 
4.6%
e2219
 
4.6%
Other values (4)2535
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l8582
17.8%
a6363
13.2%
m6363
13.2%
b5856
12.2%
u5856
12.2%
i3233
 
6.7%
n2726
 
5.7%
s2219
 
4.6%
g2219
 
4.6%
e2219
 
4.6%
Other values (4)2535
 
5.3%

track_duration_min
Real number (ℝ)

Distinct647
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4928047
Minimum0.07
Maximum13.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.2 KiB
2025-11-26T23:34:29.625341image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile1.99
Q12.88
median3.445
Q33.99
95-th percentile5.18
Maximum13.51
Range13.44
Interquartile range (IQR)1.11

Descriptive statistics

Standard deviation1.0579697
Coefficient of variation (CV)0.30289977
Kurtosis7.0457689
Mean3.4928047
Median Absolute Deviation (MAD)0.555
Skewness1.2846865
Sum29975.25
Variance1.1193
MonotonicityNot monotonic
2025-11-26T23:34:29.809020image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6758
 
0.7%
3.4757
 
0.7%
3.2456
 
0.7%
3.4255
 
0.6%
3.8652
 
0.6%
3.7852
 
0.6%
3.3651
 
0.6%
3.7151
 
0.6%
3.0750
 
0.6%
3.0349
 
0.6%
Other values (637)8051
93.8%
ValueCountFrequency (%)
0.071
< 0.1%
0.141
< 0.1%
0.191
< 0.1%
0.21
< 0.1%
0.211
< 0.1%
0.221
< 0.1%
0.31
< 0.1%
0.361
< 0.1%
0.371
< 0.1%
0.431
< 0.1%
ValueCountFrequency (%)
13.511
< 0.1%
13.151
< 0.1%
12.451
< 0.1%
12.41
< 0.1%
11.531
< 0.1%
11.471
< 0.1%
10.861
< 0.1%
10.311
< 0.1%
10.211
< 0.1%
9.881
< 0.1%

Interactions

2025-11-26T23:34:20.321959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:16.986657image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.644505image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.294363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.941535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.641075image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.429427image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.092411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.746674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.397593image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.051970image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.749895image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.540044image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.198037image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.851299image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.498387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.162459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.859800image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.648544image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.301248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.954093image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.601201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.276279image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.972305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.766452image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.416398image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.070191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.716576image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.399445image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.091381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.884063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:17.528267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.183995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:18.830391image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:19.522011image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-11-26T23:34:20.205587image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-11-26T23:34:29.925500image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
album_total_tracksalbum_typeartist_followersartist_popularityexplicittrack_duration_mintrack_numbertrack_popularity
album_total_tracks1.0000.2260.2990.2940.0970.1380.6290.009
album_type0.2261.0000.1910.2630.1020.1750.1900.164
artist_followers0.2990.1911.0000.9530.2870.3030.2910.419
artist_popularity0.2940.2630.9531.0000.2150.2560.2780.465
explicit0.0970.1020.2870.2151.0000.0290.0500.174
track_duration_min0.1380.1750.3030.2560.0291.0000.1690.144
track_number0.6290.1900.2910.2780.0500.1691.0000.026
track_popularity0.0090.1640.4190.4650.1740.1440.0261.000

Missing values

2025-11-26T23:34:21.164973image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T23:34:21.438887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T23:34:21.617399image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idtrack_nametrack_numbertrack_popularityexplicitartist_nameartist_popularityartist_followersartist_genresalbum_idalbum_namealbum_release_datealbum_total_tracksalbum_typetrack_duration_min
03EJS5LyekDim1Tf5rBFmZlTrippy Mane (ft. Project Pat)40TrueDiplo772812821moombahton5QRFnGnBeMGePBKF2xTz5zd00mscrvll, Vol. 12025-10-319album1.55
11oQW6G2ZiwMuHqlPpP27DBOMG!10TrueYelawolf642363438country hip hop, southern hip hop4SUmmwnv0xTjRcLdjczGg2OMG!2025-10-311single3.07
27mdkjzoIYlf1rx9EtBpGmUHard 2 Find14TrueRiff Raff48193302NaN3E3zEAL8gUYWaLYB9L7gbpHard 2 Find2025-10-311single2.55
367rW0Zl7oB3qEpD5YWWE5wStill Get Like That (ft. Project Pat & Starrah)830TrueDiplo772813710moombahton5QRFnGnBeMGePBKF2xTz5zd00mscrvll, Vol. 12025-10-319album1.69
415xptTfRBrjsppW0INUZjfride me like a harley20TrueRumelis488682dark r&b06FDIpSHYmZAZoyuYtc7kdcome closer / ride me like a harley2025-10-302single2.39
54ccpCcZYseq8VrPMK1EDs0BLEED12FalseMinzie467218dark r&b2NQv9p3ZQW0Ed1LB9enix8BLEED2025-10-303single2.76
63QoQ3HqXTAjgEl9LbNMbYpTe Procuro na Cidade120FalseAZERDK301657NaN1PpuOsLjPWshDLxkr0oHeUTe Procuro na Cidade2025-10-301single4.12
71YEZbdT417SfolPQzaoHs2come closer127FalseRumelis498802dark r&b06FDIpSHYmZAZoyuYtc7kdcome closer / ride me like a harley2025-10-302single2.53
84pZ949nFW5SurwzE0TSe7ICupido Vagabundo116FalseToni dos Anjos6475NaN60DLQZkzpvDvVfvEC6VOJMCupido Vagabundo2025-10-302single2.92
90L0LgwFZ7UtBnRNQvSBty6LET’S GO!133TruePsychoYP48154802nigerian drill, alté, afro adura, afrobeats, afrobeat, afroswing3ARxksm8CspGeAaZZB1v2wLET’S GO!2025-10-281single2.40
track_idtrack_nametrack_numbertrack_popularityexplicitartist_nameartist_popularityartist_followersartist_genresalbum_idalbum_namealbum_release_datealbum_total_tracksalbum_typetrack_duration_min
85724fQMGlCawbTkH9yPPZ49kPGreen Onions169FalseBooker T. & the M.G.'s54339450NaN2aGFVLz0oQPa3uxCfq9lcUGreen Onions1962-06-3012album2.93
85730Jw3cPBXlGnA6DEJrZSTI0Soul Bossa Nova156FalseQuincy Jones56456253soul jazz, big band, quiet storm1KSOL1jvue2lfcdsNZ7YN8Big Band Bossa Nova1962-01-0110album2.77
85743OnCnEWgy79xR5pr2kv4TXMisirlou447FalseDick Dale41137984surf rock7bGmjO5Cthm1SNOwUhLwKEWanna Surf?1960-12-1019album2.26
85752QfiRTz5Yc8DdShCxG1tB2Johnny B. Goode674FalseChuck Berry632211249rockabilly, rock and roll6eedtCtCjibu80yOhylSGLBerry Is On Top1959-07-0112album2.69
857668FTJoO8edSpzuYb6lGW6PSaddle Tramp1458FalseMarty Robbins61626733classic country, outlaw country3kQpBS26lAj0A0VGl1snRlGunfighter Ballads And Trail Songs1959-06-3015album2.05
85770AQquaENerGps8BQmbPw14Big Iron171FalseMarty Robbins60626733classic country, outlaw country3kQpBS26lAj0A0VGl1snRlGunfighter Ballads And Trail Songs1959-06-3015album3.92
85784f8hBeMXMvssn6HtFAtbloEl Paso1064FalseMarty Robbins61626733classic country, outlaw country3kQpBS26lAj0A0VGl1snRlGunfighter Ballads And Trail Songs1959-06-3015album4.32
85790Vy7wsXNFrbNc6UTWoScnMOver the Rainbow58FalseThe Mystics2718184doo-wop2ifB9Xjp9DdpqLlYlY60QWPresenting The Mystics1959-02-119compilation2.28
8580760clbeDBWmBsBLbszWuNZI'm A Man255FalseBo Diddley44333376blues, classic blues, rock and roll, rockabilly1cbtDEwxCjMhglb49OgNBRBo Diddley1958-01-0112album2.74
858161GEP8lryEfcuEgBMbRmNiAlone And Forsaken1145FalseHank Williams551067339classic country, honky tonk, traditional country, outlaw country, country, christian country3cusZESjkIDnDXyQwbpSsTMoanin' The Blues (Expanded Edition)1952-09-1212album2.02